Broadcast response prioritization and engagements

ABSTRACT

A computerized method comprising using at least one hardware processor for receiving a plurality of digital responses in response to a digital broadcast, each digital response associated with at least one of a plurality of computerized devices. The method comprises an action of calculating a plurality of priority scores, one each for some of the plurality of digital responses, and an action of selecting an ordered subset of the plurality of digital responses based on the plurality of priority scores. The method comprises an action of presenting to a user the ordered subset, and an action of receiving at least one digital counter-response for at least one of the plurality of digital response. The method comprises an action of sending the at least one digital counter-response to a respective one of the plurality of computerized devices associated with the respective digital response.

BACKGROUND

The invention relates to the field of operational resource management.

In many industrial and computerized systems, broadcast-response networksare used to communicate information between nodes or devices of thenetwork, such as between a central device and peripheral devices. Onenode may broadcast an electronic communication, and receive from atleast some of the other nodes a response. As a result of the response,the broadcaster may issue a counter-response, such as sending amaintenance crew to repair a faulty sensor, sending requested data,sending an acknowledgment, replying to an email, and the like

For example, maintenance tasks are collected during the day in afactory, and at night a maintenance manager will review the tasks,prioritize based on the resources, and dispatch maintenance personnel toperform the tasks based on the prioritization. This example may beperformed using a push (peripheral device initiated) or pull (centralbroadcaster initiated) technique. For example, a manager may broadcast amessage to the factory workers to report maintenance needs, and receiveresponse form the factory workers.

For example, a politician receives a large number of emails every day,optionally in response to a news article, a blog post, or the like, andassigns assistants to send email replies. For example, a user sends apost to a social network, receives a large number of responses, such ascomments and/or reactions, to the post, and sends a counter-response tosome of the responses, such as comments and/or reactions.

For example, a vehicle central processor sends a broadcast forinformation from peripheral devices of the vehicle using the controllerarea network bus (CAN-bus), and the peripheral devices (such aselectronic control units—ECUs) send the information periodically untilan acknowledgement is received.

The foregoing examples of the related art and limitations relatedtherewith are intended to be illustrative and not exclusive. Otherlimitations of the related art will become apparent to those of skill inthe art upon a reading of the specification and a study of the figures.

SUMMARY

The following embodiments and aspects thereof are described andillustrated in conjunction with systems, tools and methods which aremeant to be exemplary and illustrative, not limiting in scope.

There is provided, in accordance with an embodiment, a computerizedmethod comprising using at least one hardware processor for receiving aplurality of digital responses in response to a digital broadcast, eachdigital response associated with at least one of a plurality ofcomputerized devices. The method comprises an action of calculating aplurality of priority scores, one each for some of the plurality ofdigital responses. The method comprises an action of selecting anordered subset of the plurality of digital responses based on theplurality of priority scores, where the ordering is associated with theurgency of providing a counter-response according to the respectivepriority score. The method comprises an action of presenting to a user,using a user interface, the ordered subset. The method comprises anaction of receiving at least one digital counter-response, from the userusing the user interface, for at least one of the plurality of digitalresponse. The method comprises an action of sending the at least onedigital counter-response to a respective one of the plurality ofcomputerized devices associated with the respective digital response.

In some embodiments, the method further comprises an action of emittinga digital broadcast to the plurality of computerized devices.

In some embodiments, the method further comprises an action of receivinga plurality of profiles each associated with at least one of theplurality of computerized devices.

In some embodiments, the calculating is based on previouscounter-responses and outcomes using at least one of a statisticalanalysis, machine learning analysis, a heuristic analysis, a Monte-Carloanalysis, and a Bayesian analysis.

In some embodiments, the calculating is based on at least one of a timestamp of the broadcast, a time stamp of the response, and a currenttime.

In some embodiments, the method further comprises an action ofgenerating a suggested counter-response, wherein the suggestedcounter-response is based on previous counter-responses and outcomes,and wherein at least one of the presenting, receiving, and sending thedigital counter-response includes the suggested counter-response.

In some embodiments, the suggested counter-response is automaticallysent to the at least one of the plurality of computerized devicesassociated with the at least one of the plurality of digital reactions.

In some embodiments, the method is implemented as a system, a computerprogram product, and/or the like.

In addition to the exemplary aspects and embodiments described above,further aspects and embodiments will become apparent by reference to thefigures and by study of the following detailed description.

BRIEF DESCRIPTION OF THE FIGURES

Exemplary embodiments are illustrated in referenced figures. Dimensionsof components and features shown in the figures are generally chosen forconvenience and clarity of presentation and are not necessarily shown toscale. The figures are listed below.

FIG. 1 shows schematically a system for prioritizing responses andsending counter-responses; and

FIG. 2 shows a flowchart for prioritizing responses and sendingcounter-responses.

DETAILED DESCRIPTION

Described herein are systems, methods, and computer program products forprioritizing operational resource management. According to someembodiments, a list of digital responses is received at a central serverusing either a push or pull technique. A pull technique may be used bybroadcasting a digital message to initiate responses from other devicesof the system or network. The list of responses may be prioritizedautomatically by the server to determine the urgency and relevancy ofthe counter-responses. The prioritizing may be based on an analysis ofprevious broadcasts, responses, counter-responses, and a desired outcomethat is the goal of the broadcast and/or counter-response. A subset ofthe prioritized responses may be selected for presentation to a user,optionally with a suggested counter-response. The user may select thesuggested counter-response, manually enter a counter-response, select acounter-response from a list, or the like. Once selected, thecounter-response may be sent to the device that sent the response, orthe counter-response may be broadcasted as a new digital message.

Optionally, the analysis of previous broadcasts is one of a statisticalanalysis, a machine learning analysis, a heuristic analysis, aMonte-Carlo analysis, a Bayesian analysis or the like.

Optionally, the network is an industrial network of devices in afactory, a plant, a processing facility, a sorting facility, anindustrial multi-center company, a wide area network, and/or the like.

Optionally, the network is a computer network, a world-wide-web network,a social network, a professional network, a virtual network, a vehiclenetwork, an electronic mail network, and/or the like. For example, acruise ship has a CAN-bus network for controlling the operation of theship, and a central controller uses aspects of some embodiments toprioritize responses and send counter-responses. For example, thenetwork is a social network, such as Facebook®, Twitter®, Instagram®,and/or the like, and a broadcast is performed with a broadcasting userport to the central server from where the post is distributed tomultiple client terminals for presentation to responding users on userinterfaces. When the responding users submits a response, such as anelectronic comment, a reaction, a share, and/or the like, on the userinterface, the broadcasting user may use the prioritization technique toselect a subset of responses that may need a counter-response to achievethe goal of the initial broadcast.

Some of the examples will describe aspects of embodiments on a socialnetwork, but the aspects are relevant with minor modifications toindustrial applications, electronic messaging systems, vehicleapplications, or the like. The use of examples on social networks is notintended as limiting, but as a specific example of possible applicationin other fields.

The prioritizing may be based on an analysis of similar broadcasts,responses, counter-responses, goals, and/or the like, previouslyperformed. For example, a statistical analysis of the maintenance of afactory is done on historical maintenance data, and similarities foundto the current broadcast goals, such as minimum factory down time,minimum maintenance costs, maximum productivity, and/or the like. Forexample, a machine learning analysis is done on a large database ofvoter responses, counter-responses, goals, such as maximum number ofelection votes, maximum re-election probability, and/or the like. Themachine learning analysis may be based on the election results ofmultiple politicians across multiple regions, and therefore include alarge amount of data.

The prioritization technique is important in these systems when theamount of responses is larger than the resource capacity of theproduction of counter-responses, thus creating a backlog of responsesneeding a counter-response. Some response may not receive acounter-response in these cases, and the broadcast user may benefit fromprioritizing the responses to increase operational efficiency towardsachieving the desired goals, outcomes, and/or the like.

For example, a system follows a broadcasting user's posted contents andidentifies other user's interactions and/or responses to the postedcontents, such as comments, reactions, and/or the like. The system maycompute a score for each response, such as based on goals, engagementopportunities, the responding user profile and/or identity, time ofresponse, and/or the like. The default counter-responses for eachresponse may be additionally computed. The responses may be displayed indescending score order to the broadcasting user. Optionally, the primaryinfluencing input values to the score are displayed to the user for eachresponse. The user may send a counter-response to some responses, suchas the default counter-response, a manually inputted counter-response, acounter-response selected from a list of optional responses, and/or thelike. Optionally, the system records the selected counter-response foruse in future responses, such as scoring of future responses, futuredefault counter-responses, future counter-responses options, and/or thelike.

Currently systems support mostly manual inspection of posted content andresponses in chronological order, and when the number of responsesexceeds the resources available to counter-respond, a backlog ofresponses will be created. Being able to automatically identify and/orprioritize a subset of important responses (e.g. in a time frame) allowsa broadcasting user to counter respond to the important responses andignore the unimportant ones, according to the goals of the broadcast.When large numbers of responses are received, the automatic prioritizingtechnique avoids manually evaluating each response and manually enteringa counter-response for each. This allows the broadcasting user to focus,save time, and reduce the chances of missing an importantcounter-response opportunity, such as avoid missing engagements. Theautomatic computing of a score may be based on the goal of thebroadcasting user, such as topics of interest, motivation, receive morefeedback, operational needs, business needs, and/or the like. Theprioritization of responses may locate and present to a broadcastinguser the important responses that were previously missed, such asimportant responses that do not have a counter-response.

The prioritized responses may be automatically presented to users usingan alerting system, a server, a notification system, a user interface, astreaming filter of responses, and/or the like.

Reference is now made to FIG. 1, which shows schematically a system 100for prioritizing responses and sending counter-responses. System 100includes one or more hardware processors 101, a non-transitory computerreadable storage medium 102 (herein “storage medium”), a user interface110, and/or a network interface 120. Storage medium comprises modules ofprogram code configured to adapt the computerized system to performactions, such as with processor instructions. A Broadcaster 102A modulecomprising program code configured to cause the hardware processor(s) tooptionally broadcast a digital broadcast, such as an email, a computerbus command, a post, a blog article, an electronic document, and/or thelike. The Broadcaster 102A module comprises program code for receivingresponses from multiple devices, such as 130A, 130B, 130C, 130D, and/orthe like, on a network 130. A Prioritizer 102B program code computes foreach response a priority score based on existing data, such as a learnedhistory of counter-responses, one or more predefined goals, userprofiles, broadcaster profiles, and/or the like. Prioritizer 102Bprogram code transfers responses and priority scores to aCounter-Responder 102C module for sending a counter-response to thedevices associated with some of the responses, such as automaticallyselecting a counter-response, presenting a default counter-response to auser using user interface 110, receiving a manual counter-response froma user using user interface 110, and/or the like.

Reference is now made to FIG. 2, which shows a flowchart 200 of a methodfor prioritizing responses and sending counter-responses. Optionally, adigital message is broadcasted 201, such as a bus device command, aperipheral device command over a digital network connection, a socialmedia post, a blog content, a media post, and/or the like. Optionally,the broadcast is performed by a third party, such as a newspaperarticle, a press release, a television news item, and/or the like.Multiple responses may be received 202 in response to the broadcasted201 digital message(s). The responses are prioritized 203 by computing apriority score. The priority score is based on the goal of the broadcastor other goals associated with the broadcast, such as maintenanceefficiency, low maintenance costs, sales, public relations (i.e. when anegative news item is viewed by millions of people and they send digitalresponse to their political representative), and/or the like.Optionally, a counter-response is also computed based on an analysis ofhistorical data, user preferences, learned responses, and/or the like.When the counter-response is not sent 206 automatically 204, at leastsome of the responses are presented 205 to a user on a user interface indescending order. On the user interface the user may select 206 adefault counter-response, manually enter a counter-response, select froma list of counter-responses, and/or the like. The selection is received206 by the hardware processor(s), and sent 207 to the devices associatedwith the responses for presentation to the responding user on a userinterface of the devices.

An example algorithm for prioritization of responses and sendingcounter-responses is:

-   -   i) Extract broadcasted posts that were responded to, such as in        a predefined time frame. This extraction may include shares of        the user's contribution by others. Optionally, the time of the        broadcast is outside the time frame but the response is inside        the time frame.    -   ii) For each broadcast, compute the number of textual responses        (e.g. Comments) other posted on it, the number of reactions        posted on it (such as a like), the ratio between the number of        counter-responses the broadcasting user placed on the post        compared to the number of responses other users placed on the        post.    -   iii) Compute the broadcasting user's average counter-response        ratio rate and average ratio of broadcasting peer users with        similar amounts of posts, responses and counter-responses.    -   iv) Order the broadcasting user's posts based on stratification        of the ratio value, and then within each stratified ratio by the        creation date of the post (e.g. from most recent to oldest).    -   v) Define a subset of the posts using a static threshold and/or        using a function to determine the threshold, such as:        -   (a) When a user average ratio is less than similar the ratio            of peers, then include posts with ratio equal or higher than            the user average ratio.        -   (b) When a user average ratio greater than or equal to the            ratio of peers then include posts with ratio equal or higher            than the ratio of peers.

As used herein, a missed opportunity is defined as an electronicbroadcast (such as a message, a post, or the like) that initiated aresponse and when a counter-response was not sent by the broadcastinguser. Some missed opportunities may be best matched with a textualcounter-response from the broadcasting user, such as a comment, some maybe best matched with a reaction counter-response, such as a “like”, andothers may be best suited for no counter-response. The system evaluateseach opportunity and computes a score its potential opportunity for thebroadcasting user's goals in a personalized manner.

For example, in a system for monitoring posts and responses in apoliticians account of a social network may include a:

-   -   User profile: keeps track of what is important to the user (i.e.        goal) to counter-responding to, based on the broadcasting user's        explicit/implicit input.    -   Social Network Activity collector: stores responses to posted        contents (e.g. In a social graph).    -   Extensible opportunity score algorithm library: that has        alternative algorithms to compute scores.    -   Opportunity Interaction learner: that learns and/or analyzes        goals based on the broadcasting user's interactions to        responses, such as what/who is most important to the        broadcasting user.    -   Missed opportunities collector: identifies missed opportunities,        activate other parts to calculate their importance score (taking        into account their learned user preferences), sort opportunities        based on score, extract evidence as to why the system finds this        important and how it recommends to interact.    -   User Interface: to communicate opportunities with the user

Following are some examples use cases of important missed opportunities:

-   -   A target future customer posted a response to a broadcast, such        as a blog entry, and did not receive a counter-response.    -   A user broadcasts a status update (such as on a micro-blog) with        a question to a colleague, the colleague sent a response but the        broadcasting user did not send a counter-response.    -   Thirty people commented on a question broadcasted by a user, but        the broadcasting user did not see the comments and you should        probably put attention to some of the responses.        The system may prioritize some of the responses to assist the        broadcasting user in sending counter-responses.

Though some of the examples are based on social networks, the techniquesdescribed may be used for other applications that may benefit fromautomatic prioritization of responses to determine the highest prioritycounter-responses to send. For example, social media are computernetwork technologies that allow creation and sharing of information,such as user-generated digital content. User-generated content (UGC) maybe an electronic document, a digital image, a test message, an emoji,and/or the like. Social media differ from printed media (e.g.,magazines, newspapers, etc.) or electronic media (e.g. TV broadcasting,radio, etc.) in many ways, including quality, reach, frequency,usability, immediacy, permanence, and/or the like. Social media websitesinclude Baidu Tieba, Facebook (and its associated Facebook Messenger),Gab, Google+, Instagram, LinkedIn, Pinterest, Reddit, Snapchat, Tumblr,Twitter, Viber, WeChat, Weibo, WhatsApp, YouTube, etc.

For example, users are contributing more and more content, such asposts, in social network applications. Contributed content frequentlyreceives responses, such as reactions, likes, comments, shares, and/orthe like, from other users. The more content a user posts the moredifficult it may be to monitor other user's responses and verify thatthe user of the original post has read and counter-responded (whenneeded) to the responses, thereby engaging the users who took time torespond to the original post. In some cases, a user may post hundreds oforiginal posts in a short time, such as an hour, a day, or the like. Theoriginal posts may each generate tens of responses resulting inthousands of responses that need to be monitored and possiblycounter-responded to.

For example, when a user is running a campaign on social media, and thecampaign involves multiple social networks (i.e. Facebook, Instagram,Twitter, LinkedIn, or the like), the numbers of counter-responses neededmay by significant, such as hundreds per day, thousands per day, tens ofthousands per day, or the like.

Optionally, user of importance or interest to the broadcasting areidentified by mining the user's social network and other usersinteracted with in the past, such as by extraction of related users tothe broadcasting user based on the number of comments, number of likes,number of reactions, number of tags, and the like. Optionally, users whoare expert in a specific field are identified based on their activity,such as comments, posts, reaction, and/or the like, in social networks.An algorithm may identify for each field a weighted list of experts. Forexample, given a field and a person, searching whether the personappears in the list of experts for that field may determine the person'sexpertise level in the field.

Optionally, an influence score quantifies a user's eminence in thesocial network. Eminence score reflects how other users perceive thebroadcasting user. For example, an eminence score is based on the numberof other users trying to interact or share information with the user,follow the user, and/or the like. The influence score computation may bebased on a social graph containing all activities in the social networkand may be computed as a weighted sum of a set of counter values overthe graph.

Learning another user's topics of interest can be performed in twotasks:

-   -   Extracting the topic of each broadcast the other user acted upon        (such as a blog where the user wrote a web-page she vised in or        a post she liked)    -   Constructing, based on the topics, the user's ranked list of        topics of interest and monitoring the topic list over time

For example, extracting topics may be performed by the Latent DirichletAllocation (LDA) algorithm, such as using open source libraries (i.e.Mallet). Optionally, analysis is adapted for each data source, such asthe topic of a comment (or a reaction) may take into account the topicof the post. For example, a document of a chat may be constructed as acleansed aggregation of messages (such as per user or according to time)and only then be analyzed by LDA.

Constructing the user's list of topics of interests may be done byadding the following aspects:

-   -   Ranking the topic's importance as a weighted sum of the content        items the user acted upon—i.e., giving higher importance weight        to topic that user wrote upon than topic the user liked . . .        than topic the user viewed).    -   Add a time decay factor that prioritize new topics over older        one.    -   Combining related topics (to avoid topic “inflation”) using        synonym engines or external data sources (such as Wikipedia).        E.g., we can understand from Wikipedia ‘Options’ term that the        keywords: short, put, call and option may be all part of the        same topic.

Inputs to the computation of priority scores may include:

-   -   a) topics of interests of other users,    -   b) defined topics of interests of the broadcasting user,    -   c) learned topics of interests of the broadcasting user,    -   d) number of other user responses,    -   e) number of other user reactions,    -   f) number of the broadcasting user comments,    -   g) the other users social network influence score,    -   h) the other user's role in organization,    -   i) explicitly defined important other user,    -   j) learned importance of other user to the broadcasting user,    -   k) other user's expertise,    -   l) other user's defined topics of interest,    -   m) other user's learned topics of interest,    -   n) other user's number of times broadcast presented,    -   o) broadcasting user past counter-responses to other users, and    -   p) broadcasting user marked another user as not relevant.        Following are intermediate scores computed from the inputs.        Inputs a, b, and c may determine topics of interest (TOI) and        TOI list similarity. Inputs d, e, and f may determine quality of        missed engagement by a normalized ratio between broadcast user        activity and the activity of peers. Inputs g thru m may        determine contributor importance by a weighted sum over the set        of some inputs. Inputs n, o, and p may determine opportunity        interaction history by a weighted sum over the set of some        inputs.

The computed intermediate scores may range in values from 0 to 1 and maybe stratified as not relevant (values=0), slightly relevant (values inthe range 0 to 0.2), borderline relevant (values in the range 0.2 to0.4), somewhat relevant (values in the range 0.4 to 0.6), relevant(values in the range 0.6 to 0.8), and highly relevant (values in therange 0.8 to 1).

An interest alignment score may be computed using the equation:

${{oppotunity}\mspace{14mu}{score}} = {\sum\limits_{J = 1}^{n}\left( {{{IAA}_{J}(x)}*{WJ}*{FJ}} \right)}$where IAA_(J) denote each of the intermediate scores, WJ denotes aweighting factor for each intermediate score, and FJ denotes a userspecified importance factor for each intermediate score.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device havinginstructions recorded thereon, and any suitable combination of theforegoing. A computer readable storage medium, as used herein, is not tobe construed as being transitory signals per se, such as radio waves orother freely propagating electromagnetic waves, electromagnetic wavespropagating through a waveguide or other transmission media (e.g., lightpulses passing through a fiber-optic cable), or electrical signalstransmitted through a wire. Rather, the computer readable storage mediumis a non-transient (i.e., not-volatile) medium.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Java, Smalltalk, C++ or the like,and conventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

What is claimed is:
 1. A computerized method comprising using at leastone hardware processor for: receiving, from a broadcaster, one or morepredefined goals of a digital broadcast made by the broadcaster in asocial network; receiving a plurality of digital responses in responseto the digital broadcast, each digital response associated with at leastone of a plurality of social network users; calculating a plurality ofpriority scores, one each for some of the plurality of digitalresponses, wherein the calculating is based on the following factors:previous counter-responses, the one or more predefined goals, topics ofinterest of the broadcaster in the social network, topics of interest ofthe plurality of social network users in the social network, similarityof the topics of interest of the broadcaster to the topics of interestof the plurality of social network users, a normalized ratio betweenactivity of the broadcaster in the social network and activity of theplurality of social network users in the social network, an importanceof the broadcaster in the social network, and an importance of each ofthe plurality of social network users in the social network, wherein thecalculating uses at least one of: a machine learning analysis, aMonte-Carlo analysis, and a Bayesian analysis; selecting an orderedsubset of the plurality of digital responses based on the plurality ofpriority scores, where the ordering is associated with an urgency ofproviding a counter-response according to the respective priority score;presenting to a user, using a user interface, the ordered subset;receiving at least one digital counter-response, from the user using theuser interface, for at least one of the plurality of digital responses;and sending the at least one digital counter-response to a respectiveone of the plurality of computerized devices associated with the atleast one of the plurality of digital responses.
 2. The method accordingto claim 1, further comprising emitting a digital broadcast to theplurality of computerized devices.
 3. The method according to claim 1,further comprising receiving a plurality of profiles each associatedwith at least one of the plurality of computerized devices.
 4. Themethod according to claim 1, wherein the calculating is based on atleast one of a time stamp of the broadcast, a time stamp of theresponse, and a current time.
 5. The method according to claim 1,further comprising generating a suggested counter-response, wherein thesuggested counter-response is based on previous counter-responses andoutcomes, and wherein at least one of the presenting, receiving, andsending the at least one digital counter-response includes the suggestedcounter-response.
 6. The method according to claim 5, wherein thesuggested counter-response is automatically sent to the respective oneof the plurality of computerized devices.
 7. The method according toclaim 1, wherein: a list of the topics of interest of the broadcaster isconstructed by (a) ranking an importance of each of the topics ofinterest of the broadcaster according to how the broadcaster has actedupon the respective topic of interest in the social network, and (b)using a time decay factor to prioritize newer ones of the topics ofinterest of the broadcaster over older ones of the topics of interest ofthe broadcaster; the topics of interest of the plurality of socialnetwork users are extracted by a latent Dirichlet allocation (LDA)algorithm from comments of the plurality of social network users in thesocial network; the importance of the broadcaster is computed byanalyzing a social graph containing activities in the social network, todetermine an amount of social network users who interact with thebroadcaster in the social network; and the calculating comprises:computing intermediate scores for at least some of the factors,weighting the intermediate scores according to predefined weightingfactors, and further weighting the intermediate scores according toimportance factors specified by the broadcaster.
 8. A system comprising:at least one hardware processor; a network interface; a non-transitorycomputer-readable storage medium having program code embodied therewith,the program code executable by the at least one hardware processor to:receive, from a broadcaster, one or more predefined goals of a digitalbroadcast made by the broadcaster in a social network; receive, usingthe network interface, a plurality of digital responses in response tothe digital broadcast, each digital response associated with at leastone of a plurality of social network users; calculate a plurality ofpriority scores, one each for some of the plurality of digitalresponses, wherein the calculating is based on the following factors:previous counter-responses, the one or more predefined goals, topics ofinterest of the broadcaster in the social network, topics of interest ofthe plurality of social network users in the social network, similarityof the topics of interest of the broadcaster to the topics of interestof the plurality of social network users, a normalized ratio betweenactivity of the broadcaster in the social network and activity of theplurality of social network users in the social network, an importanceof the broadcaster in the social network, and an importance of each ofthe plurality of social network users in the social network, wherein thecalculating uses at least one of: a machine learning analysis, aMonte-Carlo analysis, and a Bayesian analysis; select an ordered subsetof the plurality of digital responses based on the plurality of priorityscores, where the ordering is associated with an urgency of providing acounter-response according to the respective priority score; present toa user, using a user interface, the ordered subset; receive at least onedigital counter-response, from the user using the user interface, for atleast one of the plurality of digital responses; and send the at leastone digital counter-response to a respective one of the plurality ofcomputerized devices associated with the at least one of the pluralityof digital responses.
 9. The system according to claim 8, wherein theprogram code is further executable to emit a digital broadcast to theplurality of computerized devices.
 10. The system according to claim 8,wherein the program code is further executable to receive a plurality ofprofiles each associated with at least one of the plurality ofcomputerized devices.
 11. The system according to claim 8, wherein thecalculating is based on at least one of a time stamp of the broadcast, atime stamp of the response, and a current time.
 12. The system accordingto claim 8, wherein the program code is further executable to generate asuggested counter-response, wherein the suggested counter-response isbased on previous counter-responses and outcomes, and wherein at leastone of the presenting, receiving, and sending the at least one digitalcounter-response includes the suggested counter-response.
 13. The systemaccording to claim 12, wherein the suggested counter-response isautomatically sent to the respective one of the plurality ofcomputerized devices.
 14. The system according to claim 8, wherein: alist of the topics of interest of the broadcaster is constructed by (a)ranking an importance of each of the topics of interest of thebroadcaster according to how the broadcaster has acted upon therespective topic of interest in the social network, and (b) using a timedecay factor to prioritize newer ones of the topics of interest of thebroadcaster over older ones of the topics of interest of thebroadcaster; the topics of interest of the plurality of social networkusers are extracted by a latent Dirichlet allocation (LDA) algorithmfrom comments of the plurality of social network users in the socialnetwork; the importance of the broadcaster is computed by analyzing asocial graph containing activities in the social network, to determinean amount of social network users who interact with the broadcaster inthe social network; and the calculating comprises: computingintermediate scores for at least some of the factors, weighting theintermediate scores according to predefined weighting factors, andfurther weighting the intermediate scores according to importancefactors specified by the broadcaster.
 15. A computer program product forprioritizing responses, the computer program product comprising anon-transitory computer-readable storage medium having program codeembodied therewith, the program code executable by at least one hardwareprocessor to: receive, from a broadcaster, one or more predefined goalsof a digital broadcast made by the broadcaster in a social network;receive, using a network interface, a plurality of digital responses inresponse to the digital broadcast, each digital response associated withat least one of a plurality of social network users; calculate aplurality of priority scores, one each for some of the plurality ofdigital responses, wherein the calculating is based on the followingfactors: previous counter-responses, the one or more predefined goals,topics of interest of the broadcaster in the social network, topics ofinterest of the plurality of social network users in the social network,similarity of the topics of interest of the broadcaster to the topics ofinterest of the plurality of social network users, a normalized ratiobetween activity of the broadcaster in the social network and activityof the plurality of social network users in the social network, animportance of the broadcaster in the social network, and an importanceof each of the plurality of social network users in the social network,wherein the calculating uses at least one of: a machine learninganalysis, a Monte-Carlo analysis, and a Bayesian analysis; select anordered subset of the plurality of digital responses based on theplurality of priority scores, where the ordering is associated with anurgency of providing a counter-response according to the respectivepriority score; present to a user, using a user interface, the orderedsubset; receive at least one digital counter-response, from the userusing the user interface, for at least one of the plurality of digitalresponses; and send the at least one digital counter-response to arespective one of the plurality of computerized devices associated withthe at least one of the plurality of digital responses.
 16. The computerprogram product according to claim 15, wherein the program code isfurther executable to emit a digital broadcast to the plurality ofcomputerized devices.
 17. The computer program product according toclaim 15, wherein the program code is further executable to receive aplurality of profiles each associated with at least one of the pluralityof computerized devices.
 18. The computer program product according toclaim 15, wherein the calculating is based on at least one of a timestamp of the broadcast, a time stamp of the response, and a currenttime.
 19. The computer program product according to claim 15, whereinthe program code is further executable to generate a suggestedcounter-response, wherein the suggested counter-response is based onprevious counter-responses and outcomes, and wherein at least one of thepresenting, receiving, and sending the at least one digitalcounter-response includes the suggested counter-response.
 20. Thecomputer program product according to claim 15, wherein: a list of thetopics of interest of the broadcaster is constructed by (a) ranking animportance of each of the topics of interest of the broadcasteraccording to how the broadcaster has acted upon the respective topic ofinterest in the social network, and (b) using a time decay factor toprioritize newer ones of the topics of interest of the broadcaster overolder ones of the topics of interest of the broadcaster; the topics ofinterest of the plurality of social network users are extracted by alatent Dirichlet allocation (LDA) algorithm from comments of theplurality of social network users in the social network; the importanceof the broadcaster is computed by analyzing a social graph containingactivities in the social network, to determine an amount of socialnetwork users who interact with the broadcaster in the social network;and the calculating comprises: computing intermediate scores for atleast some of the factors, weighting the intermediate scores accordingto predefined weighting factors, and further weighting the intermediatescores according to importance factors specified by the broadcaster.